The second category models the relationships between agents by graphs and propagates messages through the edges. Graph Neural Network [1]} learns message passing
over the edges and the joint state-action value, which requires additional losses and might not be feasible in practice. Coordination Graph [2]}
is proposed to decompose the individual state-action value into a utility function and a payoff function based on the messages of connected agents. Deep
Coordination Graph [3]} applies deep neural networks and parameter sharing to deal with large state and action spaces. However, the graph
is usually pre-defined in these methods. DICG [4]} uses the attention mechanism to learn the appropriate message-dependent coordination graph
structure with soft edge weights. GA-Comm argues [5]} that soft attention makes the agent still rely on irrelevant agents' messages and proposes
a game abstraction mechanism to extract relationships.
